Deep learning for time series classification: a review
Abstract Time Series Classification (TSC) is an important and challenging problem in data
mining. With the increase of time series data availability, hundreds of TSC algorithms have …
mining. With the increase of time series data availability, hundreds of TSC algorithms have …
K-means and alternative clustering methods in modern power systems
As power systems evolve by integrating renewable energy sources, distributed generation,
and electric vehicles, the complexity of managing these systems increases. With the …
and electric vehicles, the complexity of managing these systems increases. With the …
TS-CHIEF: a scalable and accurate forest algorithm for time series classification
Abstract Time Series Classification (TSC) has seen enormous progress over the last two
decades. HIVE-COTE (Hierarchical Vote Collective of Transformation-based Ensembles) is …
decades. HIVE-COTE (Hierarchical Vote Collective of Transformation-based Ensembles) is …
Time series classification using diversified ensemble deep random vector functional link and resnet features
WX Cheng, PN Suganthan, R Katuwal - Applied Soft Computing, 2021 - Elsevier
Abstract Random Vector Functional Link (RVFL) is popular among researchers in many
areas of machine learning. RVFL is preferred by many researchers as RVFL can produce …
areas of machine learning. RVFL is preferred by many researchers as RVFL can produce …
Unsupervised deep learning for IoT time series
Internet of Things (IoT) time-series analysis has found numerous applications in a wide
variety of areas, ranging from health informatics to network security. Nevertheless, the …
variety of areas, ranging from health informatics to network security. Nevertheless, the …
Semi-supervised time series classification by temporal relation prediction
H Fan, F Zhang, R Wang, X Huang… - ICASSP 2021-2021 IEEE …, 2021 - ieeexplore.ieee.org
Semi-supervised learning (SSL) has proven to be a powerful algorithm in different domains
by leveraging unlabeled data to mitigate the reliance on the tremendous annotated data …
by leveraging unlabeled data to mitigate the reliance on the tremendous annotated data …
Deep neural network approach for fault detection and diagnosis during startup transient of liquid-propellant rocket engine
SY Park, J Ahn - Acta Astronautica, 2020 - Elsevier
We propose a fault detection and diagnosis (FDD) method for liquid-propellant rocket
engine tests during startup transient based on deep learning. A numerical model describing …
engine tests during startup transient based on deep learning. A numerical model describing …
Time series analysis and modeling to forecast: A survey
F Dama, C Sinoquet - arXiv preprint arXiv:2104.00164, 2021 - arxiv.org
Time series modeling for predictive purpose has been an active research area of machine
learning for many years. However, no sufficiently comprehensive and meanwhile …
learning for many years. However, no sufficiently comprehensive and meanwhile …
Deep learning based inverse model for building fire source location and intensity estimation
Effective fire detection provides early warnings and key information for first responders and
people trapped insides. The idea of integrating sensor data and fire modeling presents a …
people trapped insides. The idea of integrating sensor data and fire modeling presents a …
Classification of chaotic time series with deep learning
We use standard deep neural networks to classify univariate time series generated by
discrete and continuous dynamical systems based on their chaotic or non-chaotic …
discrete and continuous dynamical systems based on their chaotic or non-chaotic …